scholarly journals On the Market-Neutrality of Optimal Pairs-Trading Strategies

2016 ◽  
Author(s):  
Bahman Angoshtari
Keyword(s):  
Trading Strategies ◽  
Pairs Trading

Optimal Pairs Trading Strategies: a Stochastic Mean-Variance Approach

2021 ◽  
Author(s):  
Fenghui Yu ◽  
Wai-Ki Ching ◽  
Chufang WU ◽  
Jiawen Gu
Keyword(s):  
Trading Strategies ◽  
Pairs Trading ◽  
Mean Variance ◽  
Variance Approach

scholarly journals Optimizing the Pairs-Trading Strategy Using Deep Reinforcement Learning with Trading and Stop-Loss Boundaries

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Taewook Kim ◽  
Ha Young Kim
Keyword(s):  
Reinforcement Learning ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Cointegration Test ◽  
Optimum Level ◽  
Pairs Trading ◽  
Proposed Model ◽  
The Mean ◽  
Stop Loss ◽  
The Given

Many researchers have tried to optimize pairs trading as the numbers of opportunities for arbitrage profit have gradually decreased. Pairs trading is a market-neutral strategy; it profits if the given condition is satisfied within a given trading window, and if not, there is a risk of loss. In this study, we propose an optimized pairs-trading strategy using deep reinforcement learning—particularly with the deep Q-network—utilizing various trading and stop-loss boundaries. More specifically, if spreads hit trading thresholds and reverse to the mean, the agent receives a positive reward. However, if spreads hit stop-loss thresholds or fail to reverse to the mean after hitting the trading thresholds, the agent receives a negative reward. The agent is trained to select the optimum level of discretized trading and stop-loss boundaries given a spread to maximize the expected sum of discounted future profits. Pairs are selected from stocks on the S&P 500 Index using a cointegration test. We compared our proposed method with traditional pairs-trading strategies which use constant trading and stop-loss boundaries. We find that our proposed model is trained well and outperforms traditional pairs-trading strategies.

INVESTIGATION OF STOCHASTIC PAIRS TRADING STRATEGIES UNDER DIFFERENT VOLATILITY REGIMES

2010 ◽  
Vol 78 ◽  
pp. 114-134 ◽  
Author(s):  
SAYAT R. BARONYAN ◽  
İ. İLKAY BODUROĞLU ◽  
EMRAH ŞENER
Keyword(s):  
Trading Strategies ◽  
Pairs Trading ◽  
Volatility Regimes

The profitability of pairs trading strategies: distance, cointegration and copula methods

2016 ◽  
Vol 16 (10) ◽  
pp. 1541-1558 ◽  
Author(s):  
Hossein Rad ◽  
Rand Kwong Yew Low ◽  
Robert Faff
Keyword(s):  
Trading Strategies ◽  
Pairs Trading

scholarly journals Optimal pairs trading with dynamic mean-variance objective

2021 ◽  
Vol 94 (1) ◽  
pp. 145-168
Author(s):  
Dong-Mei Zhu ◽  
Jia-Wen Gu ◽  
Feng-Hui Yu ◽  
Tak-Kuen Siu ◽  
Wai-Ki Ching
Keyword(s):  
Empirical Studies ◽  
Trading Strategies ◽  
Securities Market ◽  
Model Estimation ◽  
Pairs Trading ◽  
Framework Model ◽  
Convergence Trading ◽  
Using Data ◽  
Mean Variance ◽  
Trading Constraints

AbstractPairs trading is a typical example of a convergence trading strategy. Investors buy relatively under-priced assets simultaneously, and sell relatively over-priced assets to exploit temporary mispricing. This study examines optimal pairs trading strategies under symmetric and non-symmetric trading constraints. Under the assumption that the price spread of a pair of correlated securities follows a mean-reverting Ornstein-Uhlenbeck(OU) process, analytical trading strategies are obtained under a mean-variance(MV) framework. Model estimation and empirical studies on trading strategies have been conducted using data on pairs of stocks and futures traded on China’s securities market. These results indicate that pairs trading strategies have fairly good performance.

scholarly journals Trading strategies with copulas

2013 ◽  
Vol 6 (1) ◽  
pp. 83-108 ◽  
Author(s):  
Yolanda Stander ◽  
Daniël Marais ◽  
Ilse Botha
Keyword(s):  
Confidence Level ◽  
Tail Dependence ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Dependence Structure ◽  
Pairs Trading ◽  
Copula Functions ◽  
Conditional Copula ◽  
Historical Relationship ◽  
Bivariate Dependence

A new approach is proposed to identify trading opportunities in the equity market by using the information contained in the bivariate dependence structure of two equities. The relationships between the equity pairs are modelled with bivariate copulas and the fitted copula structures are utilised to identify the trading opportunities. Two trading strategies are considered that take advantage of the relative mispricing between a pair of correlated stocks and involve taking a position on the stocks when they diverge from their historical relationship. The position is then reversed when the two stocks revert to their historical relationship. Only stock-pairs with relatively high correlations are considered. The dependence structures of the chosen stock-pairs very often exhibited both upper- and lower-tail dependence, which implies that copulas with the correct characteristics should be more effective than the more traditional approaches typically applied. To identify trading opportunities, the conditional copula functions are used to derive confidence intervals for the two stocks. It is shown that the number of trading opportunities is highly dependent on the confidence level and it is argued that the chosen confidence level should take the strength of the dependence between the two stocks into account. The backtest results of the pairs-trading strategy are disappointing in that even though the strategy leads to profits in most cases, the profits are largely consumed by the trading costs. The second trading strategy entails using single stock futures and it is shown to have more potential as a statistical arbitrage approach to construct a portfolio.

Cointegration-based trading: evidence on index tracking & market-neutral strategies

2016 ◽  
Vol 42 (5) ◽  
pp. 449-471 ◽  
Author(s):  
Ioannis Papantonis
Keyword(s):  
Market Efficiency ◽  
Trading Strategies ◽  
Portfolio Performance ◽  
Pairs Trading ◽  
Content Type ◽  
Statistical Arbitrage ◽  
Long Run ◽  
Wide Range ◽  
Short Run ◽  
Depth Sensitivity

Purpose – The purpose of this paper is to present an alternative approach to equity trading that is based on cointegration. If there are long-run equilibria among financial assets, a cointegration-based trading strategy can exploit profitable opportunities by capturing mean-reverting short-run deviations. Design/methodology/approach – First, the author introduces an equity indexing technique to form cointegration tracking portfolios that are able to replicate an index effectively. The author later enhances this tracking methodology in order to construct more complex portfolio-trading strategies that can be approximately market neutral. The author monitors the performance of a wide range of trading strategies under different specifications, and conducts an in-depth sensitivity analysis of the factors that affect the optimal portfolio construction. Several statistical-arbitrage tests are also carried out in order to examine whether the profitability of the cointegration-based trading strategies could indicate a market inefficiency. Findings – The author shows that under certain parameter specifications, an efficient tracking portfolio is able to produce similar patterns in terms of returns and volatility with the market. The author also finds that a successful long-short strategy of two cointegration portfolios can yield an annualized return of more than 8 percent, outperforming the benchmark and also demonstrating insignificant correlation with the market. Even though some cointegration-based pairs-trading strategies can consistently generate significant cumulative profits, yet they do not seem to converge to risk-less arbitrages, and thus the hypothesis of market efficiency cannot be rejected. Originality/value – The primary contribution of the research lies within the detailed analysis of the factors that affect the tracking-portfolio performance, thus revealing the optimal conditions that can lead to enhanced returns. Results indicate that cointegration can provide the means to successfully reproducing the risk-return profile of a benchmark and to implementing market-neutral strategies with consistent profitability. By testing for statistical arbitrage, the author also provides new evidence regarding the connection between the profit accumulation of cointegration-based pairs-trading strategies and market efficiency.

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